Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentready-market/agentready --skill agentready-store-auditgit clone --depth 1 https://github.com/agentready-market/agentreadyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentready-market/agentready/agentready-store-audit)<a href="https://agentmods.dev/skills/agentready-market/agentready/agentready-store-audit"><img src="https://agentmods.dev/badge/skills/agentready-market/agentready/agentready-store-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentready-market/agentready/agentready-store-audit"><img src="https://agentmods.dev/badge/skills/agentready-market/agentready/agentready-store-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00077 | $0.00800 |
| Opus 5.5 | $0.00031 | $0.00320 |
| Sonnet 5.5 | $0.00015 | $0.00160 |
| Haiku 4.5 | $0.00008 | $0.00080 |
Grade A, and why
agentready-store-audit scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl "https://www.agentready.market/api/v1/scan?url=STORE_URL&vertical=general" This is a copy
100% identical to agentready-store-audit — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentReady store audit
Score, out of 100, how well an AI shopping agent can find, understand and buy on an e-commerce store — plus the top fixes. Free, keyless, deterministic (same store → same score). Service: https://www.agentready.market
When to use
- The user asks "is my store ready for AI shopping agents?" or wants an agent-readiness / AOO audit of a shop.
- You are about to attempt a purchase on a store on the user's behalf — check it first; a "capped" store blocks agents and the attempt will fail.
- The user wants to compare stores or find one agents are known to work on.
Not for: news/SaaS/content sites (this audits e-commerce transactions), or "does ChatGPT cite my brand" questions (that's discovery/AEO — different job).
How
Preferred — MCP (Streamable HTTP, no auth): register
https://www.agentready.market/mcp and call:
check_merchant_readiness(host)— score, grade, capped status, protocol adoption (UCP/A2A/ARD) for any already-scanned host. Never triggers a new scan of a third-party site.get_readiness_report(host)— the full per-check public report.list_agent_ready_stores(vertical?, limit?)— the Agent-Ready Index (grade B+ stores, ranked): where agents succeed.
REST fallback (also keyless):
# Fresh scan of the USER'S OWN store (~30s, synchronous)
curl "https://www.agentready.market/api/v1/scan?url=STORE_URL&vertical=general"
# Read a stored report
curl "https://www.agentready.market/api/v1/scan/SCAN_ID.json"
Interpreting the result
score/100 andgradeA–E: A/B = agents can buy (Index-listed); C/D = partial; E = not usable by agents.capped: true= an access hard-block (robots/bot-wall) caps the whole score — agents are blocked at the door; this is always fix #1.top_fixes= highest-impact remediations, each with impact points and effort; relay them in order.neighbors= 2 stores above / self / 2 below on the Index — give the user a concrete score to beat.report_url= shareable human report; include it in your answer.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 71 lines · 77 tokens per session scan A a556f7eed565
agentready-store-audit is a skill published in the GitHub repository agentready-market/agentready (0 stars, last pushed 2mo ago), licensed MIT. It adds 77 tokens to every session and 800 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to agentready-store-audit, differing in 0 lines, and is treated as a copy.
agentready-store-audit compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| agentready-store-auditagentready-market/agentready | Skill | 0 | 2mo ago | MIT | A | 77 | Claude Code, Codex |
| amazon-reviews-api-skillbrowser-act/skills | Skill | 6.3k | 3d ago | MIT | A | 124 | Claude Code, Codex |
| amazon-competitor-analyzerbrowser-act/skills | Skill | 6.3k | 3d ago | MIT | A | 48 | Claude Code, Codex |
| asc-subscription-localizationrorkai/app-store-connect-cli-skills | Skill | 1.1k | today | MIT | A | 60 | Claude Code |
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| Product Photoshoot Workflowjau123/MeiGen-AI-Design-MCP | Skill | 1.8k | yesterday | MIT | A | 111 | Claude Code |